A GENERALIZATION OF INVERSE MARSHALL-OLKIN FAMILY OF DISTRIBUTIONS

K. Jayakumar ； K. K. Sankaran

《Journal of Data Science》 18卷1期 (2020/01) Pp. 1-43

https://doi.org/10.6339/JDS.202001_18(1).0001

摘要

We introduce a new family of distributions namely inverse truncated discrete Linnik G family of distributions. This family is a generalization of inverse Marshall-Olkin family of distributions, inverse family of distributions generated through truncated negative binomial distribution and inverse family of distributions generated through truncated discrete Mittag-Leffler distribution. A particular member of the family, inverse truncated negative binomial Weibull distribution is studied in detail. The shape properties of the probability density function and hazard rate, model identifiability, moments, median, mean deviation, entropy, distribution of order statistics, stochastic ordering property, mean residual life function and stress-strength properties of the new generalized inverse Weibull distribution are studied. The unknown parameters of the distribution are estimated using maximum likelihood method, product spacing method and least square method. The existence and uniqueness of the maximum likelihood estimates are proved. Simulation is carried out to illustrate the performance of maximum likelihood estimates of model parameters. An AR(1) minification model with this distribution as marginal is developed. The inverse truncated negative binomial Weibull distribution is fitted to a real data set and it is shown that the distribution is more appropriate for modeling in comparison with some other competitive models.

關鍵字

Distribution of order statistics ； Entropy ； Inverse Weibull distribution ； Marshall-Olkin family of distributions ； Maximum likelihood ； Truncated negative binomial distribution ； Weibull distribution